Modeling method for characterizing condenser system based on multi-fidelity data and transfer learning

张逸佳; 任少君; 朱保宇; 翁琪航; 卫子涵; 司风琪

doi:10.19666/j.rlfd.202501029

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Modeling method for characterizing condenser system based on multi-fidelity data and transfer learning

更新时间：2025-11-21

- Modeling method for characterizing condenser system based on multi-fidelity data and transfer learning
- Thermal Power Generation Issue 11, Pages: 107-116(2025)
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- DOI：10.19666/j.rlfd.202501029
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- Published：2025
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张逸佳, 任少君, 朱保宇, et al. Modeling method for characterizing condenser system based on multi-fidelity data and transfer learning[J]. Thermal Power Generation, 2025, (11): 107-116.
DOI：

张逸佳, 任少君, 朱保宇, et al. Modeling method for characterizing condenser system based on multi-fidelity data and transfer learning[J]. Thermal Power Generation, 2025, (11): 107-116. DOI： 10.19666/j.rlfd.202501029.

摘要

数据驱动模型有效性取决于训练样本是否完备，对于样本覆盖范围外的工况，模型泛化能力较差。为构建适应机组宽负荷变化的凝汽器系统特性模型，训练样本需包括较大变化范围的发电负荷和环境温度。然而，新建机组因投运时间短，无法提供完备数据集。因此，针对不完备数据集提出了一种基于多保真度数据和迁移学习的凝汽器系统特性建模方法。该方法首先采用同类型系统的完备数据集构建预训练模型，在预训练模型上，新增线性和非线性校准网络，通过新建机组的不完备数据对校准网络进行更新，使预训练模型迁移适配不完备数据集的特征空间。最后通过某1 000 MW超临界机组凝汽器系统验证该方法有效性，验证结果表明，在训练样本不充分的情况下，该方法能准确预测凝汽器压力和循环水出口温度等参数，决定系数R~2平均值为0.95，明显优于传统单一数据集数据驱动模型的R~2平均值0.81。

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